SummaryThe detection of primordial gravity waves created during the Big Bang ranks among the greatest potential intellectual achievements in modern science. During the last few decades, the instrumental progress necessary to achieve this has been nothing short of breathtaking, and we today are able to measure the microwave sky with better than one-in-a-million precision. However, from the latest ultra-sensitive experiments such as BICEP2 and Planck, it is clear that instrumental sensitivity alone will not be sufficient to make a robust detection of gravitational waves. Contamination in the form of astrophysical radiation from the Milky Way, for instance thermal dust and synchrotron radiation, obscures the cosmological signal by orders of magnitude. Even more critically, though, are second-order interactions between this radiation and the instrument characterization itself that lead to a highly non-linear and complicated problem.
I propose a ground-breaking solution to this problem that allows for joint estimation of cosmological parameters, astrophysical components, and instrument specifications. The engine of this method is called Gibbs sampling, which I have already applied extremely successfully to basic CMB component separation. The new and ciritical step is to apply this method to raw time-ordered observations observed directly by the instrument, as opposed to pre-processed frequency maps. While representing a ~100-fold increase in input data volume, this step is unavoidable in order to break through the current foreground-induced systematics floor. I will apply this method to the best currently available and future data sets (WMAP, Planck, SPIDER and LiteBIRD), and thereby derive the world's tightest constraint on the amplitude of inflationary gravitational waves. Additionally, the resulting ancillary science in the form of robust cosmological parameters and astrophysical component maps will represent the state-of-the-art in observational cosmology in years to come.

The detection of primordial gravity waves created during the Big Bang ranks among the greatest potential intellectual achievements in modern science. During the last few decades, the instrumental progress necessary to achieve this has been nothing short of breathtaking, and we today are able to measure the microwave sky with better than one-in-a-million precision. However, from the latest ultra-sensitive experiments such as BICEP2 and Planck, it is clear that instrumental sensitivity alone will not be sufficient to make a robust detection of gravitational waves. Contamination in the form of astrophysical radiation from the Milky Way, for instance thermal dust and synchrotron radiation, obscures the cosmological signal by orders of magnitude. Even more critically, though, are second-order interactions between this radiation and the instrument characterization itself that lead to a highly non-linear and complicated problem.
I propose a ground-breaking solution to this problem that allows for joint estimation of cosmological parameters, astrophysical components, and instrument specifications. The engine of this method is called Gibbs sampling, which I have already applied extremely successfully to basic CMB component separation. The new and ciritical step is to apply this method to raw time-ordered observations observed directly by the instrument, as opposed to pre-processed frequency maps. While representing a ~100-fold increase in input data volume, this step is unavoidable in order to break through the current foreground-induced systematics floor. I will apply this method to the best currently available and future data sets (WMAP, Planck, SPIDER and LiteBIRD), and thereby derive the world's tightest constraint on the amplitude of inflationary gravitational waves. Additionally, the resulting ancillary science in the form of robust cosmological parameters and astrophysical component maps will represent the state-of-the-art in observational cosmology in years to come.

Max ERC Funding

1 999 205 €

Duration

Start date: 2018-04-01, End date: 2023-03-31

Project acronymCIVICS

ProjectCriminality, Victimization and Social Interactions

Researcher (PI)Katrine Vellesen LOKEN

Host Institution (HI)NORGES HANDELSHOYSKOLE

Call DetailsStarting Grant (StG), SH1, ERC-2017-STG

SummaryA large social science literature tries to describe and understand the causes and consequences of crime, usually focusing on individuals’ criminal activity in isolation. The ambitious aim of this research project is to establish a broader perspective of crime that takes into account the social context in which it takes place. The findings will inform policymakers on how to better use funds both for crime prevention and the rehabilitation of incarcerated criminals.
Criminal activity is often a group phenomenon, yet little is known about how criminal networks form and what can be done to break them up or prevent them from forming in the first place. Overlooking victims of crime and their relationships to criminals has led to an incomplete and distorted view of crime and its individual and social costs. While a better understanding of these social interactions is crucial for designing more effective anti-crime policy, existing research in criminology, sociology and economics has struggled to identify causal effects due to data limitations and difficult statistical identification issues.
This project will push the research frontier by combining register datasets that have never been merged before, and by using several state-of-the-art statistical methods to estimate causal effects related to criminal peer groups and their victims. More specifically, we aim to do the following:
-Use recent advances in network modelling to describe the structure and density of various criminal networks and study network dynamics following the arrest/incarceration or death of a central player in a network.
-Obtain a more accurate measure of the societal costs of crime, including actual measures for lost earnings and physical and mental health problems, following victims and their offenders both before and after a crime takes place.
-Conduct a randomized controlled trial within a prison system to better understand how current rehabilitation programs affect criminal and victim networks.

A large social science literature tries to describe and understand the causes and consequences of crime, usually focusing on individuals’ criminal activity in isolation. The ambitious aim of this research project is to establish a broader perspective of crime that takes into account the social context in which it takes place. The findings will inform policymakers on how to better use funds both for crime prevention and the rehabilitation of incarcerated criminals.
Criminal activity is often a group phenomenon, yet little is known about how criminal networks form and what can be done to break them up or prevent them from forming in the first place. Overlooking victims of crime and their relationships to criminals has led to an incomplete and distorted view of crime and its individual and social costs. While a better understanding of these social interactions is crucial for designing more effective anti-crime policy, existing research in criminology, sociology and economics has struggled to identify causal effects due to data limitations and difficult statistical identification issues.
This project will push the research frontier by combining register datasets that have never been merged before, and by using several state-of-the-art statistical methods to estimate causal effects related to criminal peer groups and their victims. More specifically, we aim to do the following:
-Use recent advances in network modelling to describe the structure and density of various criminal networks and study network dynamics following the arrest/incarceration or death of a central player in a network.
-Obtain a more accurate measure of the societal costs of crime, including actual measures for lost earnings and physical and mental health problems, following victims and their offenders both before and after a crime takes place.
-Conduct a randomized controlled trial within a prison system to better understand how current rehabilitation programs affect criminal and victim networks.

Max ERC Funding

1 187 046 €

Duration

Start date: 2018-03-01, End date: 2023-02-28

Project acronymCODE

ProjectCoincidence detection of proteins and lipids in regulation of cellular membrane dynamics

Researcher (PI)Harald STENMARK

Host Institution (HI)UNIVERSITETET I OSLO

Call DetailsAdvanced Grant (AdG), LS3, ERC-2017-ADG

SummarySpecific recruitment of different proteins to distinct intracellular membranes is fundamental in the biology of eukaryotic cells, but the molecular basis for specificity is incompletely understood. This proposal investigates the hypothesis that coincidence detection of proteins and lipids constitutes a major mechanism for specific recruitment of proteins to intracellular membranes in order to control cellular membrane dynamics. CODE will establish and validate mathematical models for coincidence detection, identify and functionally characterise novel coincidence detectors, and engineer artificial coincidence detectors as novel tools in cell biology and biotechnology.

Specific recruitment of different proteins to distinct intracellular membranes is fundamental in the biology of eukaryotic cells, but the molecular basis for specificity is incompletely understood. This proposal investigates the hypothesis that coincidence detection of proteins and lipids constitutes a major mechanism for specific recruitment of proteins to intracellular membranes in order to control cellular membrane dynamics. CODE will establish and validate mathematical models for coincidence detection, identify and functionally characterise novel coincidence detectors, and engineer artificial coincidence detectors as novel tools in cell biology and biotechnology.

Max ERC Funding

2 500 000 €

Duration

Start date: 2019-01-01, End date: 2023-12-31

Project acronymDeCode

ProjectDendrites and memory: role of dendritic spikes in information coding by hippocampal CA3 pyramidal neurons

SummaryThe hippocampus is essential for building episodic memories. Coding of locations, contexts or events in the hippocampus is based on the correlated activity of neuronal ensembles; however, the mechanisms promoting the recruitment of individual neurons into information-coding ensembles are poorly understood.
In particular, the recurrent synaptic network of pyramidal cells (PCs) in the hippocampal CA3 area, receiving external inputs from the entorhinal cortex and the dentate gyrus, is thought to be essential for associative memory. Current models of the associative functions of CA3 are mainly based on plasticity of these synaptic connections. Recent work by us and others however suggests that active, voltage-dependent properties of CA3PC dendrites may also promote ensemble functions. Dendritic voltage-dependent ion channels allow nonlinear amplification of spatiotemporally correlated synaptic inputs (such as those produced by ensemble activity) and can even generate local dendritic spikes, which may elicit specific action potential patterns and induce synaptic plasticity. Furthermore, dendritic processing may be modulated by activity-dependent regulation of dendritic ion channels. However, still little is known about the active properties of CA3PC dendrites and their functions during spatial coding or memory tasks.
The general aim of my research program is to understand the cellular mechanisms that underlie the formation of hippocampal memory-coding neuronal ensembles. Specifically, we will test the hypothesis that active input integration by dendrites of individual CA3PCs plays an important role in their recruitment into specific context-coding ensembles. By combining in vitro (patch-clamp electrophysiology and two-photon (2P) microscopy in slices) and in vivo (2P imaging and activity-dependent labelling in behaving rodents) approaches, we will provide an in-depth understanding of the dendritic components contributing to the generation of the CA3 ensemble code.

The hippocampus is essential for building episodic memories. Coding of locations, contexts or events in the hippocampus is based on the correlated activity of neuronal ensembles; however, the mechanisms promoting the recruitment of individual neurons into information-coding ensembles are poorly understood.
In particular, the recurrent synaptic network of pyramidal cells (PCs) in the hippocampal CA3 area, receiving external inputs from the entorhinal cortex and the dentate gyrus, is thought to be essential for associative memory. Current models of the associative functions of CA3 are mainly based on plasticity of these synaptic connections. Recent work by us and others however suggests that active, voltage-dependent properties of CA3PC dendrites may also promote ensemble functions. Dendritic voltage-dependent ion channels allow nonlinear amplification of spatiotemporally correlated synaptic inputs (such as those produced by ensemble activity) and can even generate local dendritic spikes, which may elicit specific action potential patterns and induce synaptic plasticity. Furthermore, dendritic processing may be modulated by activity-dependent regulation of dendritic ion channels. However, still little is known about the active properties of CA3PC dendrites and their functions during spatial coding or memory tasks.
The general aim of my research program is to understand the cellular mechanisms that underlie the formation of hippocampal memory-coding neuronal ensembles. Specifically, we will test the hypothesis that active input integration by dendrites of individual CA3PCs plays an important role in their recruitment into specific context-coding ensembles. By combining in vitro (patch-clamp electrophysiology and two-photon (2P) microscopy in slices) and in vivo (2P imaging and activity-dependent labelling in behaving rodents) approaches, we will provide an in-depth understanding of the dendritic components contributing to the generation of the CA3 ensemble code.

Max ERC Funding

1 990 314 €

Duration

Start date: 2018-06-01, End date: 2023-05-31

Project acronymFAIR

ProjectFairness and the Moral Mind

Researcher (PI)Bertil TUNGODDEN

Host Institution (HI)NORGES HANDELSHOYSKOLE

Call DetailsAdvanced Grant (AdG), SH1, ERC-2017-ADG

SummaryThe project provides a comprehensive and groundbreaking approach to the analysis of the moral mind and inequality acceptance. The first part of the project will provide a novel study of how the moral ideals of personal responsibility and individual freedom, which are fundamental values in most liberal societies, shape inequality acceptance. It will also provide the first experimental study of how people draw the moral circle, which is at the heart of the most pressing policy challenges facing the world today and strongly related to the question of global fairness. The second part will study how social institutions shape inequality acceptance and how it develops in childhood and adolescence, by providing two unique international studies of inequality acceptance in 60 countries across the world. These studies will provide novel insights on the distributive behavior of nationally representative samples of adults and children and on the cultural transmission of moral preferences in society. The project is rooted in behavioral and experimental economics, but will also draw on insights from other social sciences and philosophy. It will develop novel experimental paradigms to study the moral mind and the nature of inequality acceptance, including incentivized experiments on nationally representative populations, and combine structural and non-parametric empirical analysis with theory development. Taken together, the project represents a unique study of inequality acceptance in the social sciences that will address an important knowledge gap in the literature on inequality.

The project provides a comprehensive and groundbreaking approach to the analysis of the moral mind and inequality acceptance. The first part of the project will provide a novel study of how the moral ideals of personal responsibility and individual freedom, which are fundamental values in most liberal societies, shape inequality acceptance. It will also provide the first experimental study of how people draw the moral circle, which is at the heart of the most pressing policy challenges facing the world today and strongly related to the question of global fairness. The second part will study how social institutions shape inequality acceptance and how it develops in childhood and adolescence, by providing two unique international studies of inequality acceptance in 60 countries across the world. These studies will provide novel insights on the distributive behavior of nationally representative samples of adults and children and on the cultural transmission of moral preferences in society. The project is rooted in behavioral and experimental economics, but will also draw on insights from other social sciences and philosophy. It will develop novel experimental paradigms to study the moral mind and the nature of inequality acceptance, including incentivized experiments on nationally representative populations, and combine structural and non-parametric empirical analysis with theory development. Taken together, the project represents a unique study of inequality acceptance in the social sciences that will address an important knowledge gap in the literature on inequality.

SummaryOur astonishing cognitive abilities are the consequence of complex connectivity within our neuronal networks and the large functional diversity of excitable nerve cells and their synapses. Investigations over the past half a century revealed dramatic diversity in shape, size and functional properties among synapses established by distinct cell types in different brain regions and demonstrated that the functional differences are partly due to different molecular mechanisms. However, synaptic diversity is also observed among synapses established by molecularly and morphologically uniform presynaptic cells on molecularly and morphologically uniform postsynaptic cells. Our hypothesis is that quantitative molecular differences underlie the functional diversity of such synapses. We will focus on hippocampal CA1 pyramidal cell (PC) to mGluR1α+ O-LM cell synapses, which show remarkable functional and molecular heterogeneity. In vitro multiple cell patch-clamp recordings followed by quantal analysis will be performed to quantify well-defined biophysical properties of these synapses. The molecular composition of the functionally characterized single synapses will be determined following the development of a novel postembedding immunolocalization method. Correlations between the molecular content and functional properties will be established and genetic up- and downregulation of individual synaptic proteins will be conducted to reveal causal relationships. Finally, correlations of the activity history and the functional properties of the synapses will be established by performing in vivo two-photon Ca2+ imaging in head-fixed behaving animals followed by in vitro functional characterization of their synapses. Our results will reveal quantitative molecular fingerprints of functional properties, allowing us to render dynamic behaviour to billions of synapses when the connectome of the hippocampal circuit is created using array tomography.

Our astonishing cognitive abilities are the consequence of complex connectivity within our neuronal networks and the large functional diversity of excitable nerve cells and their synapses. Investigations over the past half a century revealed dramatic diversity in shape, size and functional properties among synapses established by distinct cell types in different brain regions and demonstrated that the functional differences are partly due to different molecular mechanisms. However, synaptic diversity is also observed among synapses established by molecularly and morphologically uniform presynaptic cells on molecularly and morphologically uniform postsynaptic cells. Our hypothesis is that quantitative molecular differences underlie the functional diversity of such synapses. We will focus on hippocampal CA1 pyramidal cell (PC) to mGluR1α+ O-LM cell synapses, which show remarkable functional and molecular heterogeneity. In vitro multiple cell patch-clamp recordings followed by quantal analysis will be performed to quantify well-defined biophysical properties of these synapses. The molecular composition of the functionally characterized single synapses will be determined following the development of a novel postembedding immunolocalization method. Correlations between the molecular content and functional properties will be established and genetic up- and downregulation of individual synaptic proteins will be conducted to reveal causal relationships. Finally, correlations of the activity history and the functional properties of the synapses will be established by performing in vivo two-photon Ca2+ imaging in head-fixed behaving animals followed by in vitro functional characterization of their synapses. Our results will reveal quantitative molecular fingerprints of functional properties, allowing us to render dynamic behaviour to billions of synapses when the connectome of the hippocampal circuit is created using array tomography.

Max ERC Funding

2 498 750 €

Duration

Start date: 2018-10-01, End date: 2023-09-30

Project acronymImPRESS

ProjectImaging Perfusion Restrictions from Extracellular Solid Stress

Researcher (PI)Kyrre Eeg Emblem

Host Institution (HI)OSLO UNIVERSITETSSYKEHUS HF

Call DetailsStarting Grant (StG), LS7, ERC-2017-STG

SummaryEven the perfect cancer drug must reach its target to have an effect. The ImPRESS project main objective is to develop a novel imaging paradigm coined Restricted Perfusion Imaging (RPI) to reveal - for the first time in humans - vascular restrictions in solid cancers caused by mechanical solid stress, and use RPI to demonstrate that alleviating this force will repair the cancerous microenvironment and improve therapeutic response. Delivery of anti-cancer drugs to the tumor is critically dependent on a functional vascular bed. Developing biomarkers that can measure how mechanical forces in a solid tumor impair perfusion and promotes therapy resistance is essential for treatment of disease.
The ImPRESS project is based on the following observations; (I) pre-clinical work suggests that therapies targeting the tumor microenvironment and extracellular matrix may enhance drug delivery by decompressing tumor vessels; (II) results from animal models may not be transferable because compressive forces in human tumors in vivo can be many times higher; and (III) there are no available imaging technologies for medical diagnostics of solid stress in human cancers. Using RPI, ImPRESS will conduct a comprehensive series of innovative studies in brain cancer patients to answer three key questions: (Q1) Can we image vascular restrictions in human cancers and map how the vasculature changes with tumor growth or treatment? (Q2) Can we use medical engineering to image solid stress in vivo? (Q3) Can RPI show that matrix-depleting drugs improve patient response to conventional chemo- and radiation therapy as well as new targeted therapies?
The ImPRESS project holds a unique position to answer these questions by our unrivaled experience with advanced imaging of cancer patients. With successful delivery, ImPRESS will have a direct impact on patient treatment and establish an imaging paradigm that will pave the way for new scientific knowledge on how to revitalize cancer therapies.

Even the perfect cancer drug must reach its target to have an effect. The ImPRESS project main objective is to develop a novel imaging paradigm coined Restricted Perfusion Imaging (RPI) to reveal - for the first time in humans - vascular restrictions in solid cancers caused by mechanical solid stress, and use RPI to demonstrate that alleviating this force will repair the cancerous microenvironment and improve therapeutic response. Delivery of anti-cancer drugs to the tumor is critically dependent on a functional vascular bed. Developing biomarkers that can measure how mechanical forces in a solid tumor impair perfusion and promotes therapy resistance is essential for treatment of disease.
The ImPRESS project is based on the following observations; (I) pre-clinical work suggests that therapies targeting the tumor microenvironment and extracellular matrix may enhance drug delivery by decompressing tumor vessels; (II) results from animal models may not be transferable because compressive forces in human tumors in vivo can be many times higher; and (III) there are no available imaging technologies for medical diagnostics of solid stress in human cancers. Using RPI, ImPRESS will conduct a comprehensive series of innovative studies in brain cancer patients to answer three key questions: (Q1) Can we image vascular restrictions in human cancers and map how the vasculature changes with tumor growth or treatment? (Q2) Can we use medical engineering to image solid stress in vivo? (Q3) Can RPI show that matrix-depleting drugs improve patient response to conventional chemo- and radiation therapy as well as new targeted therapies?
The ImPRESS project holds a unique position to answer these questions by our unrivaled experience with advanced imaging of cancer patients. With successful delivery, ImPRESS will have a direct impact on patient treatment and establish an imaging paradigm that will pave the way for new scientific knowledge on how to revitalize cancer therapies.

Max ERC Funding

1 499 638 €

Duration

Start date: 2018-01-01, End date: 2022-12-31

Project acronymISLAS

ProjectIsotopic links to atmopheric water's sources

Researcher (PI)Harald SODEMANN

Host Institution (HI)UNIVERSITETET I BERGEN

Call DetailsConsolidator Grant (CoG), PE10, ERC-2017-COG

SummaryThe hydrological cycle, with its feedbacks related to water vapour and clouds, is the largest source of uncertainty in weather prediction and climate models. Particularly processes that occur on scales smaller than the model grid lead to errors, which can compensate one another, making them difficult to detect and correct for. Undetectable compensating errors critically limit the understanding of hydrological extremes, the response of the water cycle to a changing climate, and the interpretation of paleoclimate records. Stable water isotopes have a unique potential to serve as the needed constraints, as they provide measures of moisture origin and of the phase change history. We have recently spearheaded a revised view of the atmospheric water cycle, which highlights the importance of connections on a regional scale. This implies that in some areas, all relevant processes can be studied on a regional scale. The Nordic Seas are an ideal case of such a natural laboratory, with distinct evaporation events, shallow transport processes, and swift precipitation formation. Together with recent technological advances in isotope measurements and in-situ sample collection, this will allow us to acquire a new kind of observational data set that will follow the history of water vapour from source to sink. The high-resolution, high-precision isotope data will provide a combined view of established and novel natural isotopic source tracers and set new benchmarks for climate models. A unique palette of sophisticated model tools will allow us to decipher, synthesize and exploit these observations, and to identify compensating errors between water cycle processes in models. In ISLAS, my team and I will thus make unprecedented use of stable isotopes to provide the sought-after constraints for an improved understanding of the hydrological cycle in nature and in climate models, leading towards improved predictions of future climate.

The hydrological cycle, with its feedbacks related to water vapour and clouds, is the largest source of uncertainty in weather prediction and climate models. Particularly processes that occur on scales smaller than the model grid lead to errors, which can compensate one another, making them difficult to detect and correct for. Undetectable compensating errors critically limit the understanding of hydrological extremes, the response of the water cycle to a changing climate, and the interpretation of paleoclimate records. Stable water isotopes have a unique potential to serve as the needed constraints, as they provide measures of moisture origin and of the phase change history. We have recently spearheaded a revised view of the atmospheric water cycle, which highlights the importance of connections on a regional scale. This implies that in some areas, all relevant processes can be studied on a regional scale. The Nordic Seas are an ideal case of such a natural laboratory, with distinct evaporation events, shallow transport processes, and swift precipitation formation. Together with recent technological advances in isotope measurements and in-situ sample collection, this will allow us to acquire a new kind of observational data set that will follow the history of water vapour from source to sink. The high-resolution, high-precision isotope data will provide a combined view of established and novel natural isotopic source tracers and set new benchmarks for climate models. A unique palette of sophisticated model tools will allow us to decipher, synthesize and exploit these observations, and to identify compensating errors between water cycle processes in models. In ISLAS, my team and I will thus make unprecedented use of stable isotopes to provide the sought-after constraints for an improved understanding of the hydrological cycle in nature and in climate models, leading towards improved predictions of future climate.

SummaryIn the last decade, machine vision has become part of the everyday life of ordinary people. Smartphones have advanced image manipulation capabilities, social media use image recognition algorithms to sort and filter visual content, and games, narratives and art increasingly represent and use machine vision techniques such as facial recognition algorithms, eye-tracking and virtual reality.
The ubiquity of machine vision in ordinary peoples’ lives marks a qualitative shift where once theoretical questions are now immediately relevant to the lived experience of ordinary people.
MACHINE VISION will develop a theory of how everyday machine vision affects the way ordinary people understand themselves and their world through 1) analyses of digital art, games and narratives that use machine vision as theme or interface, and 2) ethnographic studies of users of consumer-grade machine vision apps in social media and personal communication. Three main research questions address 1) new kinds of agency and subjectivity; 2) visual data as malleable; 3) values and biases.
MACHINE VISION fills a research gap on the cultural, aesthetic and ethical effects of machine vision. Current research on machine vision is skewed, with extensive computer science research and rapid development and adaptation of new technologies. Cultural research primarily focuses on systemic issues (e.g. surveillance) and professional use (e.g. scientific imaging). Aesthetic theories (e.g. in cinema theory) are valuable but mostly address 20th century technologies. Analyses of current technologies are fragmented and lack a cohesive theory or model.
MACHINE VISION challenges existing research and develops new empirical analyses and a cohesive theory of everyday machine vision. This project is a needed leap in visual aesthetic research. MACHINE VISION will also impact technical R&D on machine vision, enabling the design of technologies that are ethical, just and democratic.

In the last decade, machine vision has become part of the everyday life of ordinary people. Smartphones have advanced image manipulation capabilities, social media use image recognition algorithms to sort and filter visual content, and games, narratives and art increasingly represent and use machine vision techniques such as facial recognition algorithms, eye-tracking and virtual reality.
The ubiquity of machine vision in ordinary peoples’ lives marks a qualitative shift where once theoretical questions are now immediately relevant to the lived experience of ordinary people.
MACHINE VISION will develop a theory of how everyday machine vision affects the way ordinary people understand themselves and their world through 1) analyses of digital art, games and narratives that use machine vision as theme or interface, and 2) ethnographic studies of users of consumer-grade machine vision apps in social media and personal communication. Three main research questions address 1) new kinds of agency and subjectivity; 2) visual data as malleable; 3) values and biases.
MACHINE VISION fills a research gap on the cultural, aesthetic and ethical effects of machine vision. Current research on machine vision is skewed, with extensive computer science research and rapid development and adaptation of new technologies. Cultural research primarily focuses on systemic issues (e.g. surveillance) and professional use (e.g. scientific imaging). Aesthetic theories (e.g. in cinema theory) are valuable but mostly address 20th century technologies. Analyses of current technologies are fragmented and lack a cohesive theory or model.
MACHINE VISION challenges existing research and develops new empirical analyses and a cohesive theory of everyday machine vision. This project is a needed leap in visual aesthetic research. MACHINE VISION will also impact technical R&D on machine vision, enabling the design of technologies that are ethical, just and democratic.

SummaryThe importance of mixed-phase clouds (i.e. clouds in which liquid and ice may co-exist) for weather and climate has become increasingly evident in recent years. We now know that a majority of the precipitation reaching Earth’s surface originates from mixed-phase clouds, and the way cloud phase changes under global warming has emerged as a critically important climate feedback. Atmospheric aerosols may also have affected climate via mixed-phase clouds, but the magnitude and even sign of this effect is currently unknown. Satellite observations have recently revealed that cloud phase is misrepresented in global climate models (GCMs), suggesting systematic GCM biases in precipitation formation and cloud-climate feedbacks. Such biases give us reason to doubt GCM projections of the climate response to CO2 increases, or to changing atmospheric aerosol loadings. This proposal seeks to address the above issues, through a multi-angle and multi-tool approach: (i) By conducting field measurements of cloud phase at mid- and high latitudes, we seek to identify the small-scale structure of mixed-phase clouds. (ii) Large-eddy simulations will then be employed to identify the underlying physics responsible for the observed structures, and the field measurements will provide case studies for regional cloud-resolving modelling in order to test and revise state-of-the-art cloud microphysics parameterizations. (iii) GCMs, with revised microphysics parameterizations, will be confronted with cloud phase constraints available from space. (iv) Finally, the same GCMs will be used to re-evaluate the climate impact of mixed-phase clouds in terms of their contribution to climate forcings and feedbacks. Through this synergistic combination of tools for a multi-scale study of mixed-phase clouds, the proposed research has the potential to bring the field of climate science forward, from improved process-level understanding at small scales, to better climate change predictions on the global scale.

The importance of mixed-phase clouds (i.e. clouds in which liquid and ice may co-exist) for weather and climate has become increasingly evident in recent years. We now know that a majority of the precipitation reaching Earth’s surface originates from mixed-phase clouds, and the way cloud phase changes under global warming has emerged as a critically important climate feedback. Atmospheric aerosols may also have affected climate via mixed-phase clouds, but the magnitude and even sign of this effect is currently unknown. Satellite observations have recently revealed that cloud phase is misrepresented in global climate models (GCMs), suggesting systematic GCM biases in precipitation formation and cloud-climate feedbacks. Such biases give us reason to doubt GCM projections of the climate response to CO2 increases, or to changing atmospheric aerosol loadings. This proposal seeks to address the above issues, through a multi-angle and multi-tool approach: (i) By conducting field measurements of cloud phase at mid- and high latitudes, we seek to identify the small-scale structure of mixed-phase clouds. (ii) Large-eddy simulations will then be employed to identify the underlying physics responsible for the observed structures, and the field measurements will provide case studies for regional cloud-resolving modelling in order to test and revise state-of-the-art cloud microphysics parameterizations. (iii) GCMs, with revised microphysics parameterizations, will be confronted with cloud phase constraints available from space. (iv) Finally, the same GCMs will be used to re-evaluate the climate impact of mixed-phase clouds in terms of their contribution to climate forcings and feedbacks. Through this synergistic combination of tools for a multi-scale study of mixed-phase clouds, the proposed research has the potential to bring the field of climate science forward, from improved process-level understanding at small scales, to better climate change predictions on the global scale.